Distributed nonlinear consensus in the space of probability measures
Distributed nonlinear consensus in the space of probability measures
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DOI：
10.3182/20140824-6-za-1003.00341
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发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
A. Bishop;A. Doucet
中科院分区：
文献类型：
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作者：
A. Bishop;A. Doucet
本文首次在概率测度的Wasserstein度量空间中引入了分布式一致性。结果表明，只要渐近地满足弱网络连通性条件，就能保证个体的测度收敛到一个公共的测度值。在每个代理渐近实现的共同措施是一个最接近的同时所有初始代理措施的意义上，它最小化它和所有初始措施之间的Wasserstein距离的加权和。该算法在分布式估计领域具有一定的适用性。
Abstract Distributed consensus in the Wasserstein metric space of probability measures is introduced for the first time in this work. It is shown that convergence of the individual agents' measures to a common measure value is guaranteed so long as a weak network connectivity condition is satisfied asymptotically. The common measure achieved asymptotically at each agent is the one closest simultaneously to all initial agent measures in the sense that it minimises a weighted sum of Wasserstein distances between it and all the initial measures. This algorithm has applicability in the field of distributed estimation.